U.S. patent application number 12/955899 was filed with the patent office on 2012-05-31 for determining demographics based on user interaction.
Invention is credited to Rajeev Anand Kadam, Vishal Harshvardhan Sankhla.
Application Number | 20120136959 12/955899 |
Document ID | / |
Family ID | 46127364 |
Filed Date | 2012-05-31 |
United States Patent
Application |
20120136959 |
Kind Code |
A1 |
Kadam; Rajeev Anand ; et
al. |
May 31, 2012 |
DETERMINING DEMOGRAPHICS BASED ON USER INTERACTION
Abstract
Methods and systems for determining demographics based on user
interaction are provided. Demographic information is maintained in
association with certain indicators. Information regarding user
interaction related to an item of interest is accessed, and
indicators may be identified based on the user interaction
information associated with a user. It is determined that the user
belongs to one or more demographics based on the identified
indicators, and a report may be generated.
Inventors: |
Kadam; Rajeev Anand; (San
Jose, CA) ; Sankhla; Vishal Harshvardhan; (Santa
Clara, CA) |
Family ID: |
46127364 |
Appl. No.: |
12/955899 |
Filed: |
November 29, 2010 |
Current U.S.
Class: |
709/217 ;
707/705 |
Current CPC
Class: |
G06F 16/9535 20190101;
G06F 16/95 20190101; G06F 17/30867 20130101; G06Q 10/10
20130101 |
Class at
Publication: |
709/217 ;
707/705 |
International
Class: |
G06F 17/30 20060101
G06F017/30; G06F 15/16 20060101 G06F015/16 |
Claims
1. A method for determining demographics based on user interaction,
the method comprising: maintaining information regarding a
plurality of demographics in memory, each demographic associated
with a plurality of indicators; accessing information regarding
user interaction related to an item of interest; and executing
instructions stored in memory, wherein execution of the
instructions by a processor: identifies one or more indicators
based on the user interaction information associated with a user,
determines that the user belongs to one or more demographics based
on the identified indicators, and generates a report including the
determination that the user belongs to the one or more
demographics.
2. The method of claim 1, wherein identifying the one or more
indicators includes accessing a profile of the user associated with
the user interaction information and identifying the one or more
indicators from the profile.
3. The method of claim 2, wherein the profile associated with the
user includes information regarding user interaction with other
items.
4. The method of claim 1, further comprising consulting a reference
to determine a likelihood that the indicators are associated with
the demographic, wherein the identification of the indicators is
based on the likelihood.
5. The method of claim 1, wherein the item of interest is published
content and wherein the user interaction information related to the
published content is accessed over a communication network from one
or more data sources.
6. The method of claim 4, wherein accessing the user interaction
information includes identifying the published content and metadata
associated with the published content.
7. The method of claim 1, wherein the item of interest is a
physical object and wherein accessing the user interaction
information related to the physical object includes receiving
information regarding the user interaction as detected by a sensor
associated with the physical object.
8. The method of claim 1, wherein the item of interest is a
business and wherein accessing the user interaction information
related to the business includes receiving information from a
customer at a location associated with the business.
9. The method of claim 1, wherein accessing the user interaction
information includes use of a camera and wherein identifying the
one or more indicators includes executing recognition software to
identify characteristics of one or more subjects photographed by
the camera.
10. A system for determining demographics based on user
interaction, the system comprising: a memory for maintaining
information regarding a plurality of demographics in memory, each
demographic associated with a plurality of indicators; an interface
for accessing information regarding user interaction with an item
of interest; and a processor for executing instructions stored in
memory, wherein execution of the instructions by the processor:
identifies one or more indicators in the user interaction
information associated with a user, determines that the user
belongs to one or more demographics based on the identified
indicators, and generates a report including the determination that
the user belongs to the one or more demographics.
11. The system of claim 10, wherein the interface is further
configured to access a profile of the user associated with the user
interaction information and wherein identification of the one or
more indicators includes identifying the one or more indicators
from the profile.
12. The system of claim 11, wherein the profile associated with the
user includes information regarding user interaction with other
items.
13. The system of claim 10, wherein further execution of
instructions by the processor determines that each of the stored
plurality of demographics in memory is associated with the
plurality of indicators, each determination based on a likelihood
that the indicators are associated with the demographic.
14. The system of claim 10, wherein the item of interest is
published content and wherein the interface accesses the user
interaction information related to the published content over a
communication network from one or more data sources.
15. The system of claim 10, wherein the item of interest is a
physical object and wherein the interface includes a sensor for
detecting user interaction associated with the physical object.
16. The system of claim 10, wherein the item of interest is a
business and wherein the interface receives information from a
customer at a location associated with the business.
17. The system of claim 10, wherein the interface includes a camera
and further comprising recognition software stored in memory and
executable by the processor to identify characteristics of one or
more subjects photographed by the camera.
18. A non-transitory computer-readable storage medium having
embodied thereon a program, the program being executable by a
processor to perform a method for determining demographics based on
user interaction, the method comprising: maintaining information
regarding a plurality of demographics in memory, each demographic
associated with a plurality of indicators; accessing information
regarding user interaction related to an item of interest;
identifying one or more indicators based on the user interaction
information associated with a user; determining that the user
belongs to one or more demographics based on the identified
indicators; and generating a report including the determination
that the user belongs to the one or more demographics.
19. The non-transitory computer-readable storage medium of claim
18, wherein the item of interest is published content and wherein
the user interaction information related to the published content
is accessed over a communication network from one or more data
sources.
20. The non-transitory computer-readable storage medium of claim
18, wherein the item of interest is a physical object and wherein
accessing the user interaction information related to the physical
object includes receiving information regarding the user
interaction as detected by a sensor associated with the physical
object.
21. The non-transitory computer-readable storage medium of claim
18, wherein the item of interest is a business and wherein
accessing the user interaction information related to the business
includes receiving information from a customer at a location
associated with the business.
Description
BACKGROUND OF THE INVENTION
[0001] 1. Field of the Invention
[0002] The present invention generally relates to data analysis.
More specifically, the present invention relates to determining
demographics based on user interaction.
[0003] 2. Description of the Related Art
[0004] Many modern websites and media outlets have a social or
interactive aspect incorporated in their design. Around the world,
billions of people consume video, news, and interact with games and
each of those platforms allows users to interact with the content
based on what is displayed on the screen. For example, more than a
half billion users generate large amounts of data each day on U.S.
social networks such as Twitter.RTM. and Facebook.RTM.. Other types
of media websites (e.g., news sites) also include a social or
interactive aspect where readers can comment, respond, or otherwise
interact with the content on the site. Such content may include not
only the originally published article, photo, video, etc., but also
content posted by other users related to the original publication.
For example, a news site may publish an article. In response, a
user may provide feedback or questions in the comment section of
that article. Responsive to the first user, other users may post
their own feedback, answers to the question, or additional content
to supplement the discussion.
[0005] While such user-generated data adds value to the content,
the demographic breakdowns of the users interacting with the
content are not well understood. Presently available ways to
determining demographic information may involve, for example,
determining a user's IP address. A user's IP Address may allow for
geo-location of the user at a particular longitude and latitude.
Based on the location, census data can be accessed, allowing for
deduction as to likely demographic. Such a process may be
error-prone, however, because the location of IP addresses is
determined based on registration information, which may not
necessarily be the location of the user. In addition, census
information may be years out of date, since polling does not occur
every years and demographic data may become out-of-date.
[0006] Another method of determining demographic involves a user's
email address. An email address may be used as a marker to retrieve
the user's social graph. A user may have an account on a social
network, for example, and have provided demographic information
(e.g., in a profile). Reliance on email is also highly error-prone,
as users may not wish to provide email addresses and may therefore
fail to provide one. In some instances, users may use a fake or
back-up email address. Further, in some cases, having an email
address may not be sufficient to access the user's profile (e.g.,
due to the user electing certain privacy options).
[0007] Cookies are often employed as a way to determine user
demographics. A cookie may be downloaded to a user's computer, for
example. If the user visits another site and provides demographics
data, the demographic profile of that particular visitor may be
aggregated. For example, if user A logs into site X and then goes
to site Y and logs in and enters in their age, income, and
education background, the subsequent visit to site X could provide
the owner of site X a demographic picture of that visitor that was
not available. While accurate data may be gleaned over time, it
relies on the user to voluntarily provide relevant information.
There may also be difficulties running the cookies on certain
websites due to privacy and security concerns.
[0008] Some entities use registration and profiles to track
information on their users. A website may require a user to
register and fill out a profile in order to access and view
content. Alternatively, a website may encourage users to register
and fill out profiles by offering free access to desired content or
some other incentive. Either way, demographic information may be
determined based on the profiles provided by the users who log into
the system to access the content. For example, a website can
account for demographics based on the profiles of logged-in users
who access a video posted on the website. This approach is limited,
however, because not all websites require users to register and
provide profile information. In some cases, users may be turned off
by the extra steps required to register and fill out even a basic
profile. Even already-registered users may not want to take the
steps of logging in. For example, a user may not access content on
a site often and may consequently forget their log-in name and
password.
[0009] There is therefore a need for a robust method for
determining accurate and timely demographic information.
SUMMARY OF THE INVENTION
[0010] Embodiments of the present invention provide methods and
systems for determining demographics based on user interaction.
Demographic information is stored in memory in association with
certain indicators. Information regarding user interaction related
to an item of interest is accessed, and indicators may be
identified based on the user interaction information associated
with a user. It is determined that the user belongs to one or more
demographics based on the identified indicators, and a report may
be generated.
[0011] Some embodiments include methods for determining
demographics based on user interaction. Such methods may include
maintaining information regarding a plurality of demographics in
memory. Each demographic may be associated with a plurality of
indicators. Methods may further include accessing information
regarding user interaction related to an item of interest,
identifying indicators in the user interaction information
associated with a user, determining that the user belongs to one or
more demographics based on the identified indicators, and
generating a report including the determination that the user
belongs to the one or more demographics.
[0012] Additional embodiments include systems for discerning human
intent based on user-generated metadata. Such systems may include a
memory for maintaining information regarding a plurality of
demographics in association with indicators, an interface for
accessing information regarding user interaction with an item of
interest and a processor for executing instructions to identify one
or more indicators in the user interaction information associated
with a user, determine that the user belongs to one or more
demographics based on the identified indicators, and generate a
report including the determination that the user belongs to the one
or more demographics. In some instances, the system may include a
sensor, camera, and/or recognition software executable to identify
characteristics of subjects being sensed or photographed.
[0013] In further embodiments of the present invention,
computer-readable storage media is provided. Embodied on such
computer-readable storage media may be a program that is executable
by a processor to perform a method for determining demographics
based on user interaction.
BRIEF DESCRIPTION OF THE DRAWINGS
[0014] FIG. 1A illustrates a network digital environment in which a
system for determining demographics based on user interaction may
be implemented.
[0015] FIG. 1B illustrates an alternative environment in which a
system for determining demographics based on user interaction may
be implemented.
[0016] FIG. 2 is a flowchart illustrating an exemplary method for
determining demographics based on user interaction.
DETAILED DESCRIPTION
[0017] Methods and systems for determining demographic information
from user interactions are provided. Demographic information is
stored in memory in association with certain indicators.
Information regarding user interaction related to an item of
interest is accessed, and indicators may be identified based on the
user interaction information associated with a user. It is
determined that the user belongs to one or more demographics based
on the identified indicators, and a report may be generated.
[0018] Various embodiments of the present invention involve
examination of user interaction in almost any context, including
online interaction with published content (e.g., video,
photographs, blogs, articles, advertising), physical interaction
with physical objects (e.g., gaming consoles, smart appliances),
and feedback regarding a real or online business (e.g., kiosks). As
such, a user may be an individual or an automated piece of
technology that simulates or automates human behavioral
processes
[0019] FIG. 1A illustrates a network digital environment 100A in
which a system for determining demographic information from user
interactions may be implemented. Communicating via communication
network 110, users 120 interact with content published on various
social media sites 130A-130D. Information regarding the user
interaction may be accessed and evaluated by data analysis servers
140, whose analyses may rely on information provided by references
150A-150C.
[0020] Communication network 110 may be inclusive of any local,
proprietary network (e.g., an intranet), as well as any larger
wide-area network. The communications network 110 may include a
local area network (LAN), for example, which may be communicatively
coupled to a wide area network (WAN) such as the Internet. The
Internet is a broad network of interconnected computers and servers
allowing for the transmission and exchange of Internet Protocol
(IP) data between users connected through a network service
provider. Examples of network service providers are the public
switched telephone network, a cable service provider, a provider of
digital subscriber line (DSL) services, or a satellite service
provider. Communications network 110 allows for communication
between the various components of digital environment 100.
[0021] Users 120 may communicate through communication network 110
using any number of different electronic client or end-user devices
such as a general-purpose computer, a mobile device such as a
cellular phone, smartphone, a personal digital assistant (PDA), as
well as a netbook (i.e., a miniature laptop computing device). Such
users 120 may include any individual or group of individuals who
voluntarily provides information to various forums.
[0022] Such forums may include various social media sites (e.g.,
social media sites 130A-130D). Social media sites generally allow
users 120 to publish photographs, video, textual information (e.g.,
status updates, hyperlinks, bookmarks, blogs, microblogs, forum
posts, comments), and otherwise interact with content (including
content published by other users). Social media sites further allow
users 120 interact with the media content on the sites and provide
information regarding their daily activities, political views,
cravings, product complaints, family issues, and general interests
and musings. Media that may allow for and even solicit user
interaction include videos, photos, news articles, and games.
Common interactions may include commenting, indications of "Like,"
and sharing it with friends (e.g., reposting, "retweet"ing),
submission to aggregator sites (e.g., Digg, del.ici.ous), and
creating and sharing related content (e.g., video, art,
photographs). Such interactions may create data (and metadata) that
may be used to determine various characteristics of the users.
[0023] Social media sites generally supports and encourages such
interaction in order to boost use and engagement among users 120.
Information regarding such interactions may, either explicitly or
implicitly, be used to discern user demographics. Such data can be
aggregated from multiple data sources and analyzed to understand
the demographic breakdown of the audience, as well as offer an
extrapolated view of the total audience interacting with the type
of content. For example, analyzing the generated metadata related
to a news story and cross-comparing it with harvested social media
profiles of interactive users may reveal information about age
brackets, gender, income, location, political views, and
educational background of the wider audience (including
non-interactive users) accessing the content. In some cases,
demographic information may also be included in a user profile in
some social media sites. User profiles may further include personal
information regarding age, gender, marital status, location,
education, occupation, etc., as provided by the user.
[0024] For example, a user may watch an embedded video and post
comments with feedback regarding the video. The user may even
create their own version of the video and post it, as well
soliciting and responding to comments with respect to created
video. While such interactions may be indirectly related to the
original video, such information may nevertheless be relevant and
useful for demographic determination. Information regarding such
user interactions, whether direct or indirect, with content
published in data sources 130A-130D may be aggregated and sent to
data analysis servers 140 for evaluation with respect to
demographic. Specifically, the comments and related information
regarding the user (e.g., user profile) may then be used by data
analysis servers 140 to determine the demographics to which the
user belongs.
[0025] Data analysis servers 140 can access user interaction
information published within any of the social media sites
130A-130D (e.g., by downloading a feed, such as a firehouse feed or
garden hose feed). Data analysis servers 140 may include any
computing device as is known in the art, including standard
computing components such as network and media interfaces,
non-transitory computer-readable storage (memory), and processors
for executing instructions or accessing information that may be
stored in memory. The functionalities of multiple servers may be
integrated into a single server. Any of the aforementioned servers
(or an integrated server) may take on certain client-side, cache,
or proxy server characteristics. These characteristics may depend
on the particular network placement of the server or certain
configurations of the server.
[0026] Referring to the example above, information regarding user
interaction with a video may be sent to data analysis server 140
for determination of demographics. The user's name (or username),
for example, may be analyzed to determine whether a gender. Data
analysis servers 140 may consult one or more third-party databases
(e.g., references 150A-150C) in order to determine the likelihood
that a name indicates a male or female gender (e.g., individuals
named "John" are male 99.9% of the time). In addition to name
analysis, the diction and sentence composition of the comments may
be analyzed to determine likely educational background and/or
income brackets. The comments themselves may indicate, directly or
indirectly, demographic information about the user (e.g., " . . .
here in San Francisco," "I may be 50 years old, but . . . "). Words
in the comments may be evaluated and researched in references
150A-150C to determine whether they indicate anything about the
user demographics. References 150A-150C may include any system
providing information that may be used to evaluate the user
interaction data. The data analysis servers 140 may further use
machine learning, artificial intelligence (AI), natural language
process (NLP), Bayesian filters and classifiers, and advanced
information processing systems to identify demographics from the
user interaction information.
[0027] Such information may be processed and included in
compilations sent or exported to any applications 150A-150C
associated with the partner. Such applications 150A-150C may
include CRM systems 150A (e.g., Salesforce.RTM.), bug tracking
systems application 150B (e.g., Bugzilla.RTM.), or project
management systems 150C (e.g., Rally.RTM.), which may reside within
the enterprise or exist as an online service. Other possible
applications 150 that may receive such information include
databases/data warehousing systems, reporting/analytics systems,
business intelligence systems, support management systems, human
resources systems, and project/product management systems.
[0028] FIG. 1B illustrates an alternative environment 100B in which
a system for determining demographics based on user interaction may
be implemented. Unlike environment 100A where user interactions
involves virtual interaction with content published on the Internet
(e.g., using a computing device), user interaction with each of the
data sources 130E-130H in environment 100B involves physical
interaction at some interface. The data sources (e.g., data source
130E-130H) may or may not be coupled to a data analysis server 140
over a communication network (e.g., communication network 110 in
FIG. 1A). In some instances, the data analysis may be performed by
a device or processor residing at the data source 130E-130H. In
some cases, the data regarding user interaction may be stored at
the data source 130E-130H and later transferred to another device
for data analysis, whether over a communication network, removable
memory, or some other mode of data transfer known in the art.
[0029] One example of such an alternative environment 100B may be a
game console 130E-130F. A user may physically interact with the
game console 130E-130F via a game controller, keyboards, or any
combination of peripheral input devices known in the art. When the
user has successfully completed a level of a game, the user may be
prompted for thoughts. The user may provide some form of response
by selecting from a menu of answers, entering a comment, etc. Such
a response may be analyzed using proprietary algorithms to
determine gender, age, race, and income. In some cases, the
analysis may be done by a processor in the game console 130E-130F.
Alternatively, the analyses (and/or the information underlying such
analyses) may be stored at the game console 130E-130F and later
transferred to another device for further analysis and reporting.
Mobile game console 130F may also be used to play social games
(e.g., checking in on Yelp.RTM. or Foursquare.RTM.), where the game
involves the user being in a physical location in order to
play.
[0030] Another example may involve a vending or kiosk machines at a
business. A user may patronizing a particular business may be asked
to provide feedback at a kiosk. Such a kiosk may include a
touchscreen display, keyboard, keypad, or other way to enter
feedback. In addition to analyzing such feedback for demographic
information, a kiosk may further include a camera, which may take a
photograph of the user's face. Using recognition software, the
photograph may be analyzed to determine age, gender, race, and even
mood. Either alone or in combination with the user
feedback/comments, such analysis may further be used to determine
income, location, household size, and visit frequency. A kiosk may
also keep interaction logs, which may be used to determine that a
particular user has used the machine in the past. For example, the
user may be particularly quick to navigate through screens to the
comment section.
[0031] Another embodiment may include intelligent appliances. For
example, an intelligent refrigerator may have the ability to
catalog how often the user opens the refrigerator, add new items,
and takes items out. Like the kiosk, the refrigerator may include a
camera or other type of sensor/scanner. In conjunction with
recognition software, the contents of the refrigerator may be
identified. Such information may be used to determine, among other
things, income level, eating habits, etc.
[0032] Such intelligence may also extend to other
appliances/devices. A billboard, for example, may be equipped with
a scanner capable of discerning people walking past it. In
conjunction with recognition software, such a scanner may be able
to identify and catalog facial structure, gender, age, race,
clothing, if a person is holding any items, and walking speed. Data
cataloged over a certain time period may reveal demographic
information, such as income (e.g., whether the person is holding
bags, from which shops, clothing style, watches or jewelry), race
(e.g., facial structure analysis), age (e.g., facial structure),
familial status (e.g., walking with another person or kids), and
mood (e.g., facial structure).
[0033] FIG. 2 is a flowchart illustrating an exemplary method 200
for determining demographics based on user interaction. In the
method, information regarding various demographics is stored in a
database in memory. Information regarding user interaction with
items of interest may be accessed and analyzed to identify
indicators of demographics. The user performing the interaction is
subsequently determined to belong to one or more demographics. A
report may be generated including such a determination.
[0034] In step 210, information regarding demographics and
indicators may be stored in memory. For example, characteristics of
male and female demographics may be stored in memory. When that
characteristic later appears in data being analyzed, therefore, the
likely gender demographic can be determined. In some instances, the
information may include links to external or third-party databases
to supplement demographic data. For example, a names database may
provide likely probabilities as to whether a particular name (or
user ID name) has been found to be associated with a particular
gender, ethnicity, religion, etc.
[0035] In step 220, information regarding user interaction is
accessed. Such access may be obtained, for example, via a data feed
associated with a social media site. User interaction information
may be gathered from multiple data sources, not only from different
websites, but from different parts of the same website. For
example, a user may post a comment responding to an article posted
on a friend's Facebook page, but also repost on his/her own page.
In addition, the user may post comments related to the original
article on other Facebook pages (e.g., a fan page). Whether in the
context of news, advertising, or socializing, content that allows
for interaction also allows for evaluation of the demographics of
those doing the interaction.
[0036] Information regarding user interaction may occur virtually
or physically. Where interaction occurs physically, an interface
may be used to capture information regarding the user. Such
interfaces may include cameras, scanners, and sensors. Coupled with
recognition software, information regarding the interactive user
may be analyzed to determine detailed demographic information.
[0037] In step 230, a reference may be consulted to assist in the
analysis. General and/or specialized databases may provide
additional data that may supplement the level of detail that can be
determined about users' demographics. For example, a user may use
jargon that is specific to a particular profession or industry. The
user may use a turn of phrase associated with a particular language
or country. References with information regarding such jargon or
such languages may be consulted to determine that the user is
likely to belong to particular profession and country of
origin.
[0038] In step 240, indicators may be identified in the user
interaction information. In addition to what the user explicitly
states in relation to a piece of content (e.g., in comments),
additional data and metadata may be gathered and analyzed to see
whether they shed light on demographics. For example, information
about a user's participation on all pages of a website may be
gathered and analyzed to determine waking hours, which may shed
light on geographic location and/or type of occupation.
[0039] In some instances, the additional data gathered about the
user includes profile data. Some profiles may explicitly provide
demographic data for the user. Regardless, user profiles may at
least provide additional information with which to determine
demographic data. Listed preferences and interests, for example,
may be associated with particular demographics.
[0040] Indicators may also be gathered through use of cameras,
sensors, scanners, and other detection devices known in the art.
Coupled with recognition software, which may be continually
refined, such devices may capture information that may be used to
help form a complete picture of the demographics to which a user
belongs.
[0041] In step 250, it is determined that the user belongs to one
or more demographics based on the indicators identified in step
240. The determination that a certain user interaction indicates a
certain demographic may rely, at least in part, on information
regarding demographics and indicators stored in memory. In some
case, supplemental information regarding demographics and
indicators may be provided by third-party references 150. The
determination may further rely on information about the user that
is gathered from other data sources (e.g., other pages, other
forums, other websites).
[0042] In step 260, a report is generated including the
determination that the user belongs to the demographics determined
in step 250. The particular reports that are generated may depend
on the purpose of the report. Such purposes may include market
research, planning, behavior analysis, surveys, data modeling, etc.
In some cases, data analysis servers 140 may export demographic
data as a file (e.g., Microsoft Word.RTM., Excel .RTM., PDF, XML,
JSON, SMS, email) to a recipient automatically, periodically,
and/or upon request. The particular parameters for aggregating,
organizing, and formatting such exported data may be specified by
the specific recipient requesting such information.
[0043] The present invention may be implemented in an application
that may be operable using a variety of end user devices. The
present methodologies described herein are fully intended to be
operable on a variety of devices. Computer-readable storage media
refer to any medium or media that participate in providing
instructions to a central processing unit (CPU) for execution. Such
media can take many forms, including, but not limited to,
non-volatile and volatile media such as optical or magnetic disks
and dynamic memory, respectively. Common forms of computer-readable
media include, for example, a floppy disk, a flexible disk, a hard
disk, magnetic tape, any other magnetic medium, a CD-ROM disk,
digital video disk (DVD), any other optical medium, RAM, PROM,
EPROM, a FLASHEPROM, any other memory chip or cartridge.
[0044] Various forms of transmission media may be involved in
carrying one or more sequences of one or more instructions to a CPU
for execution. A bus carries the data to system RAM, from which a
CPU retrieves and executes the instructions. The instructions
received by system RAM can optionally be stored on a fixed disk
either before or after execution by a CPU. Various forms of storage
may likewise be implemented as well as the necessary network
interfaces and network topologies to implement the same.
[0045] While various embodiments have been described above, it
should be understood that they have been presented by way of
example only, and not limitation. The descriptions are not intended
to limit the scope of the invention to the particular forms set
forth herein. To the contrary, the present descriptions are
intended to cover such alternatives, modifications, and equivalents
as may be included within the spirit and scope of the invention as
defined by the appended claims and otherwise appreciated by one of
ordinary skill in the art. Thus, the breadth and scope of a
preferred embodiment should not be limited by any of the
above-described exemplary embodiments.
* * * * *